!python --version
!pip show Numpy
Python 3.8.8 Name: numpy Version: 1.20.0 Summary: NumPy is the fundamental package for array computing with Python. Home-page: https://www.numpy.org Author: Travis E. Oliphant et al. Author-email: None License: BSD Location: c:\users\vshls\anaconda3\lib\site-packages Requires: Required-by: tifffile, tensorflow-intel, tensorboard, tables, statsmodels, seaborn, scipy, scikit-learn, scikit-image, PyWavelets, python-louvain, pyqtgraph, pyerfa, patsy, pandas, Orange3, opt-einsum, openTSNE, numexpr, numba, mpl-scatter-density, mkl-random, mkl-fft, matplotlib, imageio, imagecodecs, h5py, glue-vispy-viewers, glue-core, fast-histogram, echo, Bottleneck, bokeh, bkcharts, baycomp, astropy
#Starter Kaggle Code for Importing Data
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory
# Any results you write to the current directory are saved as output.
from pandas import read_csv
#Lets load the dataset and sample some
column_names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
data = read_csv('C:/Users/vshls/Downloads/housing.csv', header=None, delimiter=r"\s+", names=column_names)
Linear Regression Model was adopted where we can see 80% of data for training and 20% for testing by default so that model can be trained on unseen data
#Linear Regression Model and Model Information Code
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
x = data[['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT']].values
y = data['MEDV'].values
xtrain, xtest, ytrain, ytest = train_test_split(x,y)
model = LinearRegression().fit(xtrain, ytrain)
print("Linear Regression Model Information: ")
print()
print("Coefficients: ")
print(model.coef_)
print()
print("Intercept: ")
print(model.intercept_)
print()
print("Rsq: ")
print(model.score(xtrain, ytrain))
Linear Regression Model Information: Coefficients: [-1.05133732e-01 3.98923361e-02 1.65453597e-02 3.53065985e+00 -1.71467147e+01 4.62181130e+00 2.59129055e-03 -1.40234734e+00 2.92675196e-01 -1.05181259e-02 -9.35884646e-01 1.03806816e-02 -4.86407675e-01] Intercept: 29.04656523718559 Rsq: 0.7543670425846429
R squared value shows the variation in the data that our model explains - so our linear regression model explains 72.7391% of the variation in median value of occupied homes
Predictions yield a more tangible feel for how the model acts on the data, and we can extract mean squared error and root mean squared error scores which can potentially motivate F-Statistical Tests. Note that an F statistic is unitless to yield an area under a curve while RMSE is the same units as our data observations.
from sklearn.metrics import mean_squared_error, mean_squared_error
#Model Predictions on Testing Data
print("Testing Results: ")
linRegPredList = []
actualList = []
for index in range(len(xtest)):
x = xtest[index]
x = x.reshape(-1,13)
y_prediction = float(model.predict(x))
actual = ytest[index]
print("Predicted House Price: ", y_prediction, "and Actual House Price: ", actual)
linRegPredList.append(y_prediction)
actualList.append(actual)
# Calculate Mean Squared Error (MSE)
print("MSE: ")
mse = mean_squared_error(actualList, linRegPredList)
print(mse)
print("RMSE: ")
# Calculate Root Mean Squared Error (RMSE)
rmse = np.sqrt(mse)
print(rmse)
Testing Results: Predicted House Price: 32.8642326402655 and Actual House Price: 31.6 Predicted House Price: 18.988896511104652 and Actual House Price: 18.5 Predicted House Price: 28.332310863120668 and Actual House Price: 24.4 Predicted House Price: 30.815114754073733 and Actual House Price: 30.8 Predicted House Price: 36.9489792965887 and Actual House Price: 38.7 Predicted House Price: 19.485177811608366 and Actual House Price: 17.1 Predicted House Price: 25.81093301959592 and Actual House Price: 23.3 Predicted House Price: 16.06181267397333 and Actual House Price: 17.4 Predicted House Price: 8.322858091410108 and Actual House Price: 14.6 Predicted House Price: 16.042131294201774 and Actual House Price: 14.9 Predicted House Price: 17.381123058114085 and Actual House Price: 19.1 Predicted House Price: 20.166744875335446 and Actual House Price: 16.2 Predicted House Price: 26.389289645584107 and Actual House Price: 22.0 Predicted House Price: 36.150120661360624 and Actual House Price: 46.7 Predicted House Price: 37.04898031422164 and Actual House Price: 35.2 Predicted House Price: 24.54256094174378 and Actual House Price: 24.7 Predicted House Price: 18.137869014105064 and Actual House Price: 14.5 Predicted House Price: 16.70925572063745 and Actual House Price: 23.1 Predicted House Price: 22.668909582688944 and Actual House Price: 21.2 Predicted House Price: 24.541805114218135 and Actual House Price: 29.6 Predicted House Price: 21.42853495693729 and Actual House Price: 22.0 Predicted House Price: 24.701603604367786 and Actual House Price: 22.9 Predicted House Price: 26.54500040056411 and Actual House Price: 29.8 Predicted House Price: 16.96635609170652 and Actual House Price: 17.5 Predicted House Price: 22.79912206746393 and Actual House Price: 50.0 Predicted House Price: 24.37330682541128 and Actual House Price: 23.1 Predicted House Price: 8.467600049962268 and Actual House Price: 7.2 Predicted House Price: 24.884339589439573 and Actual House Price: 24.8 Predicted House Price: 13.695004927547762 and Actual House Price: 18.2 Predicted House Price: 19.02681853104347 and Actual House Price: 15.4 Predicted House Price: 20.43659910916982 and Actual House Price: 15.2 Predicted House Price: 16.640866388316347 and Actual House Price: 18.6 Predicted House Price: 11.612115524288495 and Actual House Price: 6.3 Predicted House Price: 35.23333789585979 and Actual House Price: 33.8 Predicted House Price: 20.76331997075899 and Actual House Price: 22.0 Predicted House Price: 20.512077638335224 and Actual House Price: 19.2 Predicted House Price: 21.190918447937662 and Actual House Price: 17.8 Predicted House Price: 11.845769653199092 and Actual House Price: 16.5 Predicted House Price: 17.451800084549753 and Actual House Price: 15.1 Predicted House Price: 18.1750357369338 and Actual House Price: 16.6 Predicted House Price: 27.31253074825831 and Actual House Price: 24.5 Predicted House Price: 19.40477992844811 and Actual House Price: 18.4 Predicted House Price: 27.784688878673986 and Actual House Price: 23.9 Predicted House Price: 33.919787616864 and Actual House Price: 34.9 Predicted House Price: 13.127815757415584 and Actual House Price: 13.9 Predicted House Price: 31.180209804520995 and Actual House Price: 25.1 Predicted House Price: 29.95706812811117 and Actual House Price: 24.0 Predicted House Price: 24.717732960986975 and Actual House Price: 19.4 Predicted House Price: 22.583556678470135 and Actual House Price: 20.4 Predicted House Price: 39.46599747556679 and Actual House Price: 45.4 Predicted House Price: 23.502572318738743 and Actual House Price: 21.2 Predicted House Price: 20.455211827600337 and Actual House Price: 19.3 Predicted House Price: 15.461218125059661 and Actual House Price: 20.2 Predicted House Price: 25.03424676399512 and Actual House Price: 24.0 Predicted House Price: 29.480559977234837 and Actual House Price: 25.0 Predicted House Price: 13.833386716578515 and Actual House Price: 19.7 Predicted House Price: 29.850377450065512 and Actual House Price: 30.1 Predicted House Price: 27.03252282732015 and Actual House Price: 22.8 Predicted House Price: 23.14588346633203 and Actual House Price: 25.0 Predicted House Price: 32.84596993309773 and Actual House Price: 30.3 Predicted House Price: 8.510518850136439 and Actual House Price: 11.8 Predicted House Price: 16.868829556862963 and Actual House Price: 17.8 Predicted House Price: 30.839043768417472 and Actual House Price: 32.7 Predicted House Price: 22.869770250148882 and Actual House Price: 22.9 Predicted House Price: 27.04397595900728 and Actual House Price: 23.9 Predicted House Price: 24.158782204598133 and Actual House Price: 21.7 Predicted House Price: 19.089804551882146 and Actual House Price: 18.5 Predicted House Price: 23.111675238115346 and Actual House Price: 20.2 Predicted House Price: 25.194354971495514 and Actual House Price: 28.7 Predicted House Price: 15.26401507849823 and Actual House Price: 18.9 Predicted House Price: 20.299813779413036 and Actual House Price: 20.5 Predicted House Price: 11.999012163478312 and Actual House Price: 10.8 Predicted House Price: 28.662532728691968 and Actual House Price: 31.2 Predicted House Price: 31.681954606684087 and Actual House Price: 29.9 Predicted House Price: 21.032649831417913 and Actual House Price: 19.3 Predicted House Price: 25.296756713987943 and Actual House Price: 29.6 Predicted House Price: 20.53565952398219 and Actual House Price: 16.8 Predicted House Price: 17.22286837686751 and Actual House Price: 13.4 Predicted House Price: 28.719968925211013 and Actual House Price: 26.4 Predicted House Price: 23.566243132982933 and Actual House Price: 23.0 Predicted House Price: 40.54499824789278 and Actual House Price: 21.9 Predicted House Price: 24.137688566474786 and Actual House Price: 22.8 Predicted House Price: 23.70949065024206 and Actual House Price: 20.5 Predicted House Price: 26.73106425550354 and Actual House Price: 20.6 Predicted House Price: 14.808997540777176 and Actual House Price: 15.4 Predicted House Price: 23.829386842467134 and Actual House Price: 23.4 Predicted House Price: 13.274532855405788 and Actual House Price: 10.5 Predicted House Price: 19.578661326138075 and Actual House Price: 15.0 Predicted House Price: 22.308615355462983 and Actual House Price: 21.4 Predicted House Price: 18.672811884918044 and Actual House Price: 20.1 Predicted House Price: 22.67509629120881 and Actual House Price: 21.2 Predicted House Price: 20.15866056883759 and Actual House Price: 12.5 Predicted House Price: 22.147243934959597 and Actual House Price: 20.3 Predicted House Price: 17.080565508455926 and Actual House Price: 19.4 Predicted House Price: 19.964170452711645 and Actual House Price: 20.5 Predicted House Price: 39.36701811128658 and Actual House Price: 41.7 Predicted House Price: 26.41616903988784 and Actual House Price: 23.8 Predicted House Price: 15.570783340274852 and Actual House Price: 8.4 Predicted House Price: 21.498778179895815 and Actual House Price: 23.3 Predicted House Price: 24.27013171980631 and Actual House Price: 21.7 Predicted House Price: 29.042297159260915 and Actual House Price: 23.6 Predicted House Price: 17.835456335315214 and Actual House Price: 19.6 Predicted House Price: 24.1136903031126 and Actual House Price: 17.8 Predicted House Price: 16.858369489332436 and Actual House Price: 13.1 Predicted House Price: 19.980412147928252 and Actual House Price: 18.4 Predicted House Price: 13.949088078873954 and Actual House Price: 14.5 Predicted House Price: 26.019969920455335 and Actual House Price: 26.5 Predicted House Price: 18.2255255494724 and Actual House Price: 14.1 Predicted House Price: 26.68313280381969 and Actual House Price: 15.0 Predicted House Price: 20.625050695088586 and Actual House Price: 22.9 Predicted House Price: 20.062680820016652 and Actual House Price: 19.4 Predicted House Price: 31.88477206334078 and Actual House Price: 31.1 Predicted House Price: 17.524636974520558 and Actual House Price: 14.3 Predicted House Price: 28.39983015425021 and Actual House Price: 28.0 Predicted House Price: 38.667764136101596 and Actual House Price: 37.6 Predicted House Price: 20.117731602656164 and Actual House Price: 15.3 Predicted House Price: 13.480871385180677 and Actual House Price: 13.4 Predicted House Price: 12.469248724831019 and Actual House Price: 13.6 Predicted House Price: 23.439343126231847 and Actual House Price: 18.9 Predicted House Price: 9.15153302684135 and Actual House Price: 13.2 Predicted House Price: 22.600271367147506 and Actual House Price: 16.5 Predicted House Price: 19.916229457127237 and Actual House Price: 24.3 Predicted House Price: 15.990696207678464 and Actual House Price: 17.6 Predicted House Price: 15.177412081561016 and Actual House Price: 16.2 Predicted House Price: 22.117055656880247 and Actual House Price: 21.4 Predicted House Price: 37.52447033811366 and Actual House Price: 44.0 Predicted House Price: 23.118288043310475 and Actual House Price: 19.4 MSE: 20.703611180037004 RMSE: 4.550122106057925
Scatter Plot with Line of Best Fit shown
import plotly.express as px
import plotly.graph_objects as go
import pandas as pd
linRegPerf = px.scatter(x = actualList, y = linRegPredList, trendline = "ols")
linRegPerf.show()
#x_values_y_equals_x = list(range(-10, 11))
#y_values_y_equals_x = x_values_y_equals_x
#linRegPerf = px.line(df, x="x", y="y", title="Unsorted Input")
# Create a figure for the first plot
#linRegPerf = go.Figure()
# Add a trace to the figure (e.g., a line plot)
#linRegPerf.add_trace(px.scatter(x=actualList, y=linRegPredList))
# Update the layout if needed
#linRegPerf.update_layout(title='Multiple Plots with y = x')
# Create some sample data for y = x
#x_values_y_equals_x = list(range(-10, 11))
#y_values_y_equals_x = x_values_y_equals_x
# Add a new trace for y = x
#linRegPerf.add_trace(go.Scatter(x=x_values_y_equals_x, y=y_values_y_equals_x, mode='markers', name='y = x'))
# Display the updated figure
#linRegPerf = px.scatter(x = actualList, y = linRegPredList)
#linRegPerf.show()
#fig = go.Figure(data=go.Scatter(x=actualList, y=actualList))
#fig.show()
#Using Orange Data Mining software, we learned AdaBoost is the best standard technique for this data.
from sklearn.tree import *
from sklearn.ensemble import *
rng = np.random.RandomState(1)
adaboostR = AdaBoostRegressor(DecisionTreeRegressor(max_depth=4), n_estimators=300, random_state=rng)
adaboostR.fit(xtrain, ytrain)
print("AdaBoost Model Regression Score:")
print()
print("Rsq: ")
print(adaboostR.score(xtrain, ytrain))
#from sklearn import preprocessing
#ytrainCat = preprocessing.LabelEncoder().fit_transform(y)
#adaboost = AdaBoostClassifier(n_estimators=100, base_estimator= DecisionTreeClassifier(max_depth= 1),learning_rate=1, algorithm='SAMME.R', random_state = None)
#adaboost.fit(xtrain,ytrainCat)
#print("AdaBoost Model Regression Score:")
#print()
#print("Rsq: ")
#print(adaboost.score(xtrain, ytrainCat))
#throws an error: ValueError: Found input variables with inconsistent numbers of samples: [379, 506]
AdaBoost Model Regression Score: Rsq: 0.9593838922471113
Our AdaBoost model explains 94.6611% of the variation in median value of occupied homes.
#Model Predictions on Testing Data
print("Testing Results: ")
adaBoostPredList = []
actualList = []
for index in range(len(xtest)):
x = xtest[index]
x = x.reshape(-1,13)
y_prediction = float(adaboostR.predict(x))
actual = ytest[index]
print("Predicted House Price: ", y_prediction, "and Actual House Price: ", actual)
adaBoostPredList.append(y_prediction)
actualList.append(actual)
# Calculate Mean Squared Error (MSE)
print("MSE: ")
mse = mean_squared_error(actualList, adaBoostPredList)
print(mse)
print("RMSE: ")
# Calculate Root Mean Squared Error (RMSE)
rmse = np.sqrt(mse)
print(rmse)
Testing Results: Predicted House Price: 31.608163265306146 and Actual House Price: 31.6 Predicted House Price: 20.424999999999997 and Actual House Price: 18.5 Predicted House Price: 25.961538461538456 and Actual House Price: 24.4 Predicted House Price: 26.025 and Actual House Price: 30.8 Predicted House Price: 42.96666666666666 and Actual House Price: 38.7 Predicted House Price: 21.484210526315792 and Actual House Price: 17.1 Predicted House Price: 26.79999999999999 and Actual House Price: 23.3 Predicted House Price: 15.349324324324341 and Actual House Price: 17.4 Predicted House Price: 15.25 and Actual House Price: 14.6 Predicted House Price: 14.66265060240965 and Actual House Price: 14.9 Predicted House Price: 16.596116504854383 and Actual House Price: 19.1 Predicted House Price: 20.814655172413783 and Actual House Price: 16.2 Predicted House Price: 25.8 and Actual House Price: 22.0 Predicted House Price: 44.900000000000006 and Actual House Price: 46.7 Predicted House Price: 43.425 and Actual House Price: 35.2 Predicted House Price: 24.820909090909122 and Actual House Price: 24.7 Predicted House Price: 19.88085106382979 and Actual House Price: 14.5 Predicted House Price: 19.88085106382979 and Actual House Price: 23.1 Predicted House Price: 23.164062499999964 and Actual House Price: 21.2 Predicted House Price: 25.52656250000002 and Actual House Price: 29.6 Predicted House Price: 20.11458333333334 and Actual House Price: 22.0 Predicted House Price: 23.697029702970312 and Actual House Price: 22.9 Predicted House Price: 23.43214285714288 and Actual House Price: 29.8 Predicted House Price: 18.6125 and Actual House Price: 17.5 Predicted House Price: 31.5 and Actual House Price: 50.0 Predicted House Price: 21.826881720430112 and Actual House Price: 23.1 Predicted House Price: 10.635714285714286 and Actual House Price: 7.2 Predicted House Price: 26.450588235294124 and Actual House Price: 24.8 Predicted House Price: 20.219999999999995 and Actual House Price: 18.2 Predicted House Price: 14.934482758620694 and Actual House Price: 15.4 Predicted House Price: 16.219047619047622 and Actual House Price: 15.2 Predicted House Price: 23.18863636363636 and Actual House Price: 18.6 Predicted House Price: 11.27894736842105 and Actual House Price: 6.3 Predicted House Price: 33.75517241379311 and Actual House Price: 33.8 Predicted House Price: 21.668926553672318 and Actual House Price: 22.0 Predicted House Price: 20.115584415584404 and Actual House Price: 19.2 Predicted House Price: 15.2125 and Actual House Price: 17.8 Predicted House Price: 19.21215469613259 and Actual House Price: 16.5 Predicted House Price: 14.7512396694215 and Actual House Price: 15.1 Predicted House Price: 20.359900990098982 and Actual House Price: 16.6 Predicted House Price: 25.716666666666665 and Actual House Price: 24.5 Predicted House Price: 15.9625 and Actual House Price: 18.4 Predicted House Price: 26.771428571428572 and Actual House Price: 23.9 Predicted House Price: 32.829292929292905 and Actual House Price: 34.9 Predicted House Price: 16.903030303030302 and Actual House Price: 13.9 Predicted House Price: 25.633333333333333 and Actual House Price: 25.1 Predicted House Price: 25.746715328467186 and Actual House Price: 24.0 Predicted House Price: 21.7695652173913 and Actual House Price: 19.4 Predicted House Price: 22.612500000000015 and Actual House Price: 20.4 Predicted House Price: 46.955172413793086 and Actual House Price: 45.4 Predicted House Price: 22.141406249999992 and Actual House Price: 21.2 Predicted House Price: 20.79791666666667 and Actual House Price: 19.3 Predicted House Price: 19.67142857142858 and Actual House Price: 20.2 Predicted House Price: 21.995384615384616 and Actual House Price: 24.0 Predicted House Price: 29.21935483870968 and Actual House Price: 25.0 Predicted House Price: 18.681818181818187 and Actual House Price: 19.7 Predicted House Price: 26.200000000000003 and Actual House Price: 30.1 Predicted House Price: 23.43214285714288 and Actual House Price: 22.8 Predicted House Price: 24.839393939393965 and Actual House Price: 25.0 Predicted House Price: 29.94728260869567 and Actual House Price: 30.3 Predicted House Price: 15.144444444444442 and Actual House Price: 11.8 Predicted House Price: 16.018181818181816 and Actual House Price: 17.8 Predicted House Price: 31.451999999999988 and Actual House Price: 32.7 Predicted House Price: 21.97411167512689 and Actual House Price: 22.9 Predicted House Price: 24.790666666666656 and Actual House Price: 23.9 Predicted House Price: 21.721311475409856 and Actual House Price: 21.7 Predicted House Price: 21.03 and Actual House Price: 18.5 Predicted House Price: 21.08257575757575 and Actual House Price: 20.2 Predicted House Price: 25.75 and Actual House Price: 28.7 Predicted House Price: 19.71870503597123 and Actual House Price: 18.9 Predicted House Price: 18.244444444444444 and Actual House Price: 20.5 Predicted House Price: 10.638805970149262 and Actual House Price: 10.8 Predicted House Price: 30.314018691588732 and Actual House Price: 31.2 Predicted House Price: 28.045833333333345 and Actual House Price: 29.9 Predicted House Price: 21.973333333333336 and Actual House Price: 19.3 Predicted House Price: 33.44791666666665 and Actual House Price: 29.6 Predicted House Price: 20.43157894736843 and Actual House Price: 16.8 Predicted House Price: 14.826086956521744 and Actual House Price: 13.4 Predicted House Price: 25.24705882352941 and Actual House Price: 26.4 Predicted House Price: 24.721138211382097 and Actual House Price: 23.0 Predicted House Price: 46.05499999999999 and Actual House Price: 21.9 Predicted House Price: 23.35360824742268 and Actual House Price: 22.8 Predicted House Price: 22.578181818181807 and Actual House Price: 20.5 Predicted House Price: 21.826881720430112 and Actual House Price: 20.6 Predicted House Price: 15.016666666666667 and Actual House Price: 15.4 Predicted House Price: 25.090350877193 and Actual House Price: 23.4 Predicted House Price: 9.846341463414639 and Actual House Price: 10.5 Predicted House Price: 21.995384615384616 and Actual House Price: 15.0 Predicted House Price: 20.31797752808989 and Actual House Price: 21.4 Predicted House Price: 18.457391304347833 and Actual House Price: 20.1 Predicted House Price: 20.814655172413783 and Actual House Price: 21.2 Predicted House Price: 14.931111111111116 and Actual House Price: 12.5 Predicted House Price: 22.08252427184465 and Actual House Price: 20.3 Predicted House Price: 17.13846153846154 and Actual House Price: 19.4 Predicted House Price: 21.995384615384616 and Actual House Price: 20.5 Predicted House Price: 48.07058823529412 and Actual House Price: 41.7 Predicted House Price: 22.522580645161295 and Actual House Price: 23.8 Predicted House Price: 11.37837837837838 and Actual House Price: 8.4 Predicted House Price: 24.708235294117646 and Actual House Price: 23.3 Predicted House Price: 19.980000000000008 and Actual House Price: 21.7 Predicted House Price: 25.69655172413793 and Actual House Price: 23.6 Predicted House Price: 21.540229885057474 and Actual House Price: 19.6 Predicted House Price: 17.771428571428583 and Actual House Price: 17.8 Predicted House Price: 10.773076923076925 and Actual House Price: 13.1 Predicted House Price: 20.150793650793652 and Actual House Price: 18.4 Predicted House Price: 15.987499999999999 and Actual House Price: 14.5 Predicted House Price: 26.251111111111115 and Actual House Price: 26.5 Predicted House Price: 15.287499999999998 and Actual House Price: 14.1 Predicted House Price: 20.82222222222222 and Actual House Price: 15.0 Predicted House Price: 24.147619047619028 and Actual House Price: 22.9 Predicted House Price: 19.34274193548388 and Actual House Price: 19.4 Predicted House Price: 30.080000000000002 and Actual House Price: 31.1 Predicted House Price: 15.573809523809535 and Actual House Price: 14.3 Predicted House Price: 27.853658536585378 and Actual House Price: 28.0 Predicted House Price: 48.25 and Actual House Price: 37.6 Predicted House Price: 21.68405797101448 and Actual House Price: 15.3 Predicted House Price: 13.029508196721311 and Actual House Price: 13.4 Predicted House Price: 15.455555555555557 and Actual House Price: 13.6 Predicted House Price: 21.937037037037044 and Actual House Price: 18.9 Predicted House Price: 15.784337349397592 and Actual House Price: 13.2 Predicted House Price: 24.491666666666678 and Actual House Price: 16.5 Predicted House Price: 22.60526315789475 and Actual House Price: 24.3 Predicted House Price: 21.03 and Actual House Price: 17.6 Predicted House Price: 15.25128205128207 and Actual House Price: 16.2 Predicted House Price: 19.190344827586202 and Actual House Price: 21.4 Predicted House Price: 41.76451612903224 and Actual House Price: 44.0 Predicted House Price: 21.08257575757575 and Actual House Price: 19.4 MSE: 15.399032411329177 RMSE: 3.9241600899210494
Scatterplot of predicted vs. actual gives us a better feel of the improvement between the Linear Regression and AdaBoost Models
adaBoost = px.scatter(x = actualList, y = adaBoostPredList, trendline = "ols")
adaBoost.show()